A multi-sensor track association algorithm based on entropy function of association degree

摘要

For the track association problem, the method based on statistics needs to assume the sensor data obey a typical statistical distribution. To avoid such a preset assumption, this paper proposed a new entropy function of association degree and a novel track association algorithm based on entropy function of association degree. The association degree of the track pair was acquired by constructing an entropy function of association degree at the sampling points, and the track pair with high association degree was judged to be associated. The algorithm is characterized by a concise ide-a, small computational load, and no requirements on the distribution of sensor data. The algorithm was implemented in the multi-sensor multi-target environment, and was compared with two other track association algorithms. The simulation results show the effectiveness and the superiority of the algorithm.